This invention relates to monitoring and characterization of bioelectric. potentials in muscles and related body components that arise from gestures.
Standard interface and signal input devices, such as a keyboard, mouse, light pen and/or touch screen, are now well known and well used, especially in environments that rely upon computer signal processing. However, in an environment where the operator""s body is moving, where the operator""s attention is divided while certain tasks are being performed, where extremes of high or low temperature, dim light, noise or similar interfering features are present, a standard interface may be unreliable. For certain classes of signal inputs by a (human) operator, monitoring and characterization of bioelectric potentials generated in the body by operator gestures are likely to be more reliable, if it is possible to classify and distinguish between different groups of bioelectric signals associated with different gestures.
The minimum size of a computer or other signal processor is rapidly decreasing, and the sizes of many of these processors today are already smaller than the sizes of the conventional data/command entry interfaces, such as keyboards, chording keyboards, touch screen locations and light pens, that are available for data/command entry. Development of a system that monitors and analyzes bioelectric potentials generated in the body by gestures would allow these conventional interfaces to be replaced by virtual data/command entry interfaces that are not limited by the minimum size of the interface. With such a virtual system, the operator makes a gesture that would be carried out on an interface of conventional size, if such an interface were present, and the system monitors and interprets the bioelectric potentials generated in a selected part of the operator""s body and translates these bioelectric potentials to one or more electronic commands for a selected instrument (computer, signal processor, vehicle controller, cellphone, etc.)
Classification of, and distinguishment between, different groups of bioelectric signals is especially difficult, for the following reasons: (1) the bioelectric signals for most gestures, except those involving movement of major muscle groups, usually have very low amplitudes, of the order of 0.01 xe2x88x9210 xcexcvolts; (2) a bioelectric signal amplitude is often accompanied by a dc voltage offset, which compromises the dynamic range over which a voltage sensor can operate; (3) the bioelectric signals of interest are often accompanied by other body signals that mask or interfere with recognition of the signals of interest; (4) many of the bioelectric signals of interest lie in the frequency range 30-300 Hz, and the environment is bathed in extraneous signals in the range 50-60 Hz, arising from the drive current supplied to drive most electrical instruments; (5) the bioelectric signals of interest are transported along nerve cords within the body, and the signals sensed at the surface of a body""s skin are thus filtered by the various cutaneous and subcutaneous layers a nerve cord signal must pass through to reach the surface; it is estimated that the electrical signals are attenuated by a factor of about 30 in passing through a person""s skin; and (6) physical differences, such as different hand sizes or arm lengths, different angles of application, different muscle sizes, and different amounts of layered fat adjacent to a muscle group.
What is needed is an approach that permits non-invasive, non-intrusive monitoring and analysis on a person""s body of bioelectric signals generated by each of a selected group of gestures and identification of one or more particular states that are characteristic of a particular gesture. Preferably, the analysis should be rapid and provide real time, on-line identification of a gesture that is being presently, executed by the person. Preferably, the monitoring and control should be performed by a small appliance, worn on the body, that either performs part or all of the analysis or transmits the data sensed by the appliance to a nearby processor that provides the analysis nearly instantaneously.
These needs are met by the invention, which provides a system, and a method for using the system, that senses bioelectric signals generated within a person""s body in response to performance of a gesture by the person, using two or more spaced apart electrodes located on the body. The system forms and analyzes differences of signals generated at the sensing locations in each of a sequence of preferably-overlapping time intervals, using hidden Markov modeling, neural net analysis or another suitable technique to identify a sequence of states that is associated with and/or characteristic of the bioelectric signals that accompany the gesture. An initially chosen set of coarse and fine gestures includes the following gestures using a hand and/or arm: making a stop motion; making a fist; making a come motion; making a thumb up motion; making a thumb down motion; tapping with at least one finger; reaching for and depressing at least one key on a keyboard; moving a joystick in at least one of the directions forward, backward, right and left; touching a joystick without movement of the joystick; grasping and positioning a stylus near a touch screen; and similar gestures in a three-dimensional virtual environment. The gestures to which the invention applies are not limited to this initial set, but the invention can be demonstrated using this set. The signals monitored in the invention are primarily electromyographic (EMG) signals that arise from motion of one or more muscles. Other classes of signals include electroencephalographic (EEG) and electrooculographic (EOG) signals, which are associated with signals originating in the brain and in the eye, respectively.